机器学习在表面活性剂性能预测和设计方面的研究进展

doi:10.6023/cjoc202512023

摘要/Abstract

表面活性剂作为一类重要的功能性化合物, 在日用化工、生物医药及石油开采等领域应用广泛. 然而, 其传统研发依赖于试错实验的模式, 不仅难以满足新型表面活性剂在极端条件下的性能需求, 且缺乏对环境归宿和生物安全性的系统性评估. 机器学习作为一种数据驱动的研究范式, 为表面活性剂的高效开发提供了新途径, 尤其在分子性质预测方面展现出巨大潜力. 系统综述了机器学习在预测表面活性剂关键物性(如临界胶束浓度、表面张力、亲水亲油平衡值、吸附效率及克拉夫特点)中的研究进展. 此外, 探讨了该技术在预测其生物降解性与生态毒性等环境安全属性方面的初步应用与前景. 现有研究多集中于临界胶束浓度、表面张力与亲水亲油平衡值的预测, 而对吸附效率、克拉夫特点及环境安全参数的建模研究相对有限. 通过将预测范围拓展至这些尚未被充分研究的性质, 机器学习有望成为表面活性剂理性设计、性能优化与绿色评估的关键工具, 从而推动高性能、绿色环保表面活性剂的开发.

关键词: 机器学习, 表面活性剂, 性质预测, 分子表示, 定量构效关系(QSPR)

Surfactants, as a class of important functional compounds, are widely used in daily chemicals, biomedicine, petroleum extraction, and other fields. However, the traditional trial-and-error experimental approach not only struggles to meet the performance requirements of novel surfactants under extreme conditions but also lacks systematic evaluation of their environmental fate and biosafety. Machine learning, as a data-driven research paradigm, offers a novel approach for efficient surfactant development, demonstrating significant potential particularly in molecular property prediction. This article systematically reviews advances in machine learning for predicting key surfactant properties (e.g., critical micelle concentration, surface tension, hydrophilic-lipophilic balance, adsorption efficiency, and Krafft point). Furthermore, it examines preliminary applications and prospects of this technology in predicting environmental safety parameters such as biodegradability and ecotoxicity. Current research predominantly focuses on predicting critical micelle concentration, surface tension, and hydrophilic-lipophilic balance, whereas modeling studies on adsorption efficiency, Krafft point, and environmental safety parameters remain relatively limited. By extending predictive capabilities to these underexplored properties, machine learning is poised to become a pivotal tool for rational design, performance optimization, and green assessment of surfactants, thereby advancing the development of high-performance and environmentally friendly surfactants.

Key words: Machine learning, surfactant, property prediction, molecular representation, QSPR

引用此文

张桂苹, 丁昌华, 郭勇, 李遥, 薛小松. 机器学习在表面活性剂性能预测和设计方面的研究进展[J]. 有机化学, 2026, 46(6): 2310-2326.

Guiping Zhang, Changhua Ding, Yong Guo, Yao Li, Xiaosong Xue. Research Progress of Machine Learning in Surfactant Performance Prediction and Design[J]. Chinese Journal of Organic Chemistry, 2026, 46(6): 2310-2326.

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图/表 12

图1. 表面活性剂的五个核心性质示意图

Figure 1. Schematic diagram of five core properties of surfactants

图2. 以十二烷基硫酸钠表面活性剂示例的三种分子描述符

Figure 2. Three molecular descriptors illustrated using sodium dodecyl sulfate (SDS) surfactant

Authors	Year	Algorithm	Features/Descriptors	Dataset size	Surfactant type	R² (Test)	RMSE (log CMC, mol/L) (Test)
Specific-type models
Gaudin et al.^[13]	2016	MLR	Integral; topological, compositional, and fragment descriptors	83	Sugar-based	0.910	0.320
Wang et al.^[45]	2019	MLR	Norm descriptors	101	Anionic	0.913	0.257
Jiao et al.^[46]	2020	PLS	Holographic fragment fingerprints	120	Gemini (anionic/ cationic)	0.980	0.176
Setiawan et al.^[47]	2021	Consensus	Dragon/CDK/ISIDA descriptors	231	Gemini cationic	0.870	0.350
Creton et al.^[48]	2022	SVM	Functional group descriptors	254^a	PFAS+conven- tional surfactants	0.899	0.273
General models
Zavala et al.^[49]	2021	GCN	Molecular graphs	202	All typesᵈ	0.920	0.300
Seddon et al.^[50]	2022	XGB	3D descriptors+physical constraints	154	All types	0.870	-
Striolo et al.^[30]	2023	GPs-GNN	Molecular graphs	202+43	All types	-	0.210
Boukelkal et al.^[51]	2024	DA-SVR	Dragon/Mordred descriptors+temperature	593	All types	0.986	0.144
Mitsos et al.^[52]	2024	GNN	Molecular graphs	429	All types	0.940	0.280
Mitsos et al.^[53]	2024	GNN	Molecular graphs+ temperature	1375^b	All types (multi-task)	0.950	0.240
Ge et al.^[54]	2024	LGBM (ionic)+ GBDT (nonionic)	Descriptors+ temperature+PCA classification	779	All types	0.944	0.284
Robinson et al.^[55]	2025	AttentiveFP	Molecular graphs	1395	All types (multi-task)	—	0.346
Complex conditions & mixture systems
Ham et al.^[56]	2024	GNN	Molecular graphs, MD simulations, geometric descriptors	92	All types (multi-task)	0.900	0.280
Barbosa et al.^[57]	2025	FNN	DFT-derived features+ temperature	1377	All types	0.950	0.380
Mitsos et al.^[58]	2025	Combined-GNN	Hybrid graphs, mole fraction, temperature	1924^c	Binary mixtures	0.930	0.249
Choudhary et al.^[59]	2025	ANN	Descriptor fusion	979	Binary mixtures	0.941	0.315

Authors	Year	Algorithm	Features/Descriptors	Dataset size	Surfactant type	R² (Test)	RMSE (log CMC, mol/L) (Test)
Specific-type models
Gaudin et al.^[13]	2016	MLR	Integral; topological, compositional, and fragment descriptors	83	Sugar-based	0.910	0.320
Wang et al.^[45]	2019	MLR	Norm descriptors	101	Anionic	0.913	0.257
Jiao et al.^[46]	2020	PLS	Holographic fragment fingerprints	120	Gemini (anionic/ cationic)	0.980	0.176
Setiawan et al.^[47]	2021	Consensus	Dragon/CDK/ISIDA descriptors	231	Gemini cationic	0.870	0.350
Creton et al.^[48]	2022	SVM	Functional group descriptors	254^a	PFAS+conven- tional surfactants	0.899	0.273
General models
Zavala et al.^[49]	2021	GCN	Molecular graphs	202	All typesᵈ	0.920	0.300
Seddon et al.^[50]	2022	XGB	3D descriptors+physical constraints	154	All types	0.870	-
Striolo et al.^[30]	2023	GPs-GNN	Molecular graphs	202+43	All types	-	0.210
Boukelkal et al.^[51]	2024	DA-SVR	Dragon/Mordred descriptors+temperature	593	All types	0.986	0.144
Mitsos et al.^[52]	2024	GNN	Molecular graphs	429	All types	0.940	0.280
Mitsos et al.^[53]	2024	GNN	Molecular graphs+ temperature	1375^b	All types (multi-task)	0.950	0.240
Ge et al.^[54]	2024	LGBM (ionic)+ GBDT (nonionic)	Descriptors+ temperature+PCA classification	779	All types	0.944	0.284
Robinson et al.^[55]	2025	AttentiveFP	Molecular graphs	1395	All types (multi-task)	—	0.346
Complex conditions & mixture systems
Ham et al.^[56]	2024	GNN	Molecular graphs, MD simulations, geometric descriptors	92	All types (multi-task)	0.900	0.280
Barbosa et al.^[57]	2025	FNN	DFT-derived features+ temperature	1377	All types	0.950	0.380
Mitsos et al.^[58]	2025	Combined-GNN	Hybrid graphs, mole fraction, temperature	1924^c	Binary mixtures	0.930	0.249
Choudhary et al.^[59]	2025	ANN	Descriptor fusion	979	Binary mixtures	0.941	0.315

表1. 临界胶束浓度预测任务及结果

Table 1. Task and results of critical micelle concentration prediction

表1. 临界胶束浓度预测任务及结果

Table 1. Task and results of critical micelle concentration prediction

图3. 基于154种表面活性剂数据集展示机器学习方法对未知分子的预测示例

Figure 3. Predictive examples of unknown molecules by machine learning methods based on a dataset of 154 surfactants (a) C11E7, (b) β-GlcOC8+2, (c) 1-O-dodecyl-β-D-maltoside, and (d) Na- AOT.[50]

图4. 图卷积神经网络对所有类型表面活性剂的预测结果

Figure 4. GCN predictions for all classes of surfactants Left: low-dimensional distribution of surfactant fingerprints using t-SNE. The test samples (red crosses) are widespread, and most of the designed surfactants (green points) fall outside the clusters of existing dataset. Right: parity plot between the predicted and experimental log CMC values (training data in blue and test data in red). The test set achieved a coefficient of determination (R²) of 0.92 with a root mean square error (RMSE) of 0.30 (log scale). Molecular structures are shown for the selected extreme points: Structure a is an anionic surfactant (minor outlier) with a high log CMC value; Structure b is a cationic surfactant (minor outlier) with a high log CMC value; Structure c is a zwitterionic surfactant (major outlier); Structure d is a nonionic surfactant with a low log CMC value.[49] Copyright (2021) American Chemical Society.

图5. 混合表面活性剂表面张力预测模型的多尺度特征重要性分析

Figure 5. Multiscale feature importance analysis of surface tension prediction models for mixed surfactants (a) Feature importance ranking based on XGBoost model weights, displaying the top 15 key features; (b) Permutation importance analysis (PEI) quantifying feature impacts on model performance (MSE) through 100 Monte Carlo perturbations; (c) SHAP value analysis elucidating both direction and magnitude of marginal contributions of features to predictions.[77] Copyright (2025) Elsevier B.V.

Authors	Year	Algorithm	Features/Descriptors	Dataset size	Data description	R² (Test)	RMSE (mN/m) (Test)
Gaudin et al.^[73]	2018	MLR	Quantum chemical descriptors	70	Sugar-based nonionic surfactants (cyclic/non-cyclic head groups; linear/branched/unsaturated chains)	0.780	2.400
Hemmati-Sarapardeh et al.^[76]	2023	GBRT	Temperature, n-alkane molecular weight, concentration, HLB, PIT	390	Five ionic surfactants (C10TAB, C12TAB, C14TAB, C16TAB, SDS)	0.985	1.628
Pradilla et al.^[67]	2023	RF	37 manually defined molecular descriptors	691+9	691 conventional surfactants+ 9 amino acids	0.550	4.720
Saeedi Dehaghani et al.^[74]	2023	SGBT	Temperature, mole fraction, molecular weight, density, boiling point, etc.	4010	122 binary mixtures (48 ionic liquids+20 nonionic liquids)	0.993	0.001
Ham et al.^[56]	2024	GNN	Molecular graphs, MD simulations, geometric descriptors	92	Anionic, cationic, nonionic, and zwitterionic surfactants	0.900	2.640
Robinson et al.^[55]	2025	Attentive FP	Molecular graphs	972	Anionic, cationic, nonionic, and zwitterionic surfactants	–	3.407
Ge et al.^[77]	2025	XGBoost	Mole fraction, log concentration, interaction parameters, molecular descriptors	1135	Polyether nonionic surfactants+four representative surfactants (SDS, CTAB, TX100, BS12)	0.999	0.260^a

表2. 临界胶束浓度下表面张力预测任务及结果

Table 2. Prediction task and results of surface tension at critical micelle concentration

图6. 特征选择与重要性分析

Figure 6. Feature selection and importance analysis (a) Feature correlation heatmap analysis; (b) Cumulative variance contribution rate of principal component analysis (The cumulative contribution rate of the first nine principal components exceeds 90%, while increasing the number of principal components yields no significant improvement, indicating that the first nine principal components are sufficient to retain the vast majority of data information); (c) SHAP feature importance evaluation; (d) LOFO analysis revealing contributions and synergistic effects of key descriptors (ETA_C_A, MATS7p, etc.).[82] Copyright (2025) Elsevier B.V.

图7. 表面活性剂结构-效率关系的3D可视化

Figure 7. 3D visualization of structure-efficiency relationship of surfactants The clustering results based on PCA dimensionality reduction reveal an increasing trend of pC20 values along the arrow direction. Zones A, B, and C correspond to low, medium, and high adsorption efficiencies, respectively, with structural modifications (e.g., chain length variation, headgroup type) governing the spatial distribution patterns of molecules.[82] Copyright (2025) Elsevier B.V.

图8. QSPR模型预测非离子表面活性剂HLB值的适用域分析[87]

Figure 8. Applicability domain analysis of QSPR model predicting HLB values of nonionic surfactants[87] Copyright (2020) Elsevier B.V.

图9. 糖基表面活性剂Krafft温度(TK>25 ℃)基于整体分子描述符模型的决策树预测模型[92]

Figure 9. Decision tree prediction model for the Krafft temperature (TK>25 °C) of glycoside-based surfactants based on holistic molecular descriptor modeling

图10. 案例一的Pareto三维图[101]

Figure 10. Pareto front for the first case study[101] Both SAscore and log(CMC), where CMC is in mg/L, are minimized and biodegradability maximized. The red points represent the optimal solutions

参考文献 105

[1]	Rosen, M. J. Surfactants and Interfacial Phenomena, 3rd ed., Wiley-Interscience, Hoboken, N. J. 2010.
[2]	Castro, M. J. L.; Ojeda, C.; Cirelli, A. F. Environ. Chem. Lett. 2014, 12, 85.
[3]	Liu, Y.; Zhang, H.; Zhang, W.; Binks, B. P.; Cui, Z.; Jiang, J. Angew. Chem. Int. Ed. 2023, 62, e202210050.
[4]	Puvvada, S.; Blankschtein, D. J. Chem. Phys. 1990, 92, 3710.
[5]	Reif, I.; Mulqueen, M.; Blankschtein, D. Langmuir 2001, 17, 5801.
[6]	Goldsipe, A.; Blankschtein, D. Langmuir 2006, 22, 3547.
[7]	Ren, Z. H. AIChE J. 2017, 63, 5076.
[8]	Sresht, V.; Lewandowski, E. P.; Blankschtein, D.; Jusufi, A. Lang- muir 2017, 33, 8319.
[9]	Vishnyakov, A.; Lee, M.-T.; Neimark, A. V. J. Phys. Chem. Lett. 2013, 4, 797.
[10]	Gahan, C. G.; Patel, S. J.; Boursier, M. E.; Nyffeler, K. E.; Jennings, J.; Abbott, N. L.; Blackwell, H. E.; Van Lehn, R. C.; Lynn, D. M. J. Phys. Chem. B 2020, 124, 3616.
[11]	Barraud, E.; Dalmazzone, C.; Mouret, A.; De Bruin, T.; Creton, B.; Pasquier, D.; Lachet, V.; Nieto-Draghi, C. Langmuir 2025, 41, 7272.
[12]	Huibers, P. D. T.; Lobanov, V. S.; Katritzky, A. R.; Shah, D. O.; Karelson, M. J. Colloid Interface Sci. 1997, 187, 113.
[13]	Gaudin, T.; Rotureau, P.; Pezron, I.; Fayet, G. Ind. Eng. Chem. Res. 2016, 55, 11716.
[14]	Puzyn, T.; Suzuki, N.; Haranczyk, M.; Rak, J. J. Chem. Inf. Model. 2008, 48, 1174.
[15]	Goh, G. B.; Siegel, C.; Vishnu, A.; Hodas, N. O.; Baker, N. Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-Developed QSAR/QSPR Models arXiv, 2017, https://doi.org/10.48550/arXiv.1706.06689.
[16]	Vanhaelen, Q.; Lin, Y.-C.; Zhavoronkov, A. ACS Med. Chem. Lett. 2020, 11, 1496.
[17]	Xie, W.; Wang, F.; Li, Y.; Lai, L.; Pei, J. J. Chem. Inf. Model. 2022, 62, 2269.
[18]	Du, Y.; Jamasb, A. R.; Guo, J.; Fu, T.; Harris, C.; Wang, Y.; Duan, C.; Liò P.; Schwaller, P.; Blundell, T. L. Nat. Mach. Intell. 2024, 6, 589.
[19]	Sultan, A.; Sieg, J.; Mathea, M.; Volkamer, A. J. Chem. Inf. Model. 2024, 64, 6259.
[20]	Xin, H.; Virk, A. S.; Virk, S. S.; Akin-Ige, F.; Amin, S. Curr. Opin. Colloid Interface Sci. 2024, 73, 101847.
[21]	Chen, W.; Dai, J.; Li, Y.; Zhou, J.; Gao, B., Zhao, Y.; Xu, T.; Xue, X. Chin. J. Org. Chem. 2025, 45, 2189. (in Chinese)
	(陈维明, 戴静芳, 李英勇, 周俊红, 高犇, 赵英莉, 徐挺军, 薛小松, 有机化学, 2025, 45, 2189.)
[22]	Jablonka, K. M.; Ai, Q.; Al-Feghali, A.; Badhwar, S.; Bocarsly, J. D.; Bran, A. M.; Bringuier, S.; Brinson, L. C.; Choudhary, K.; Circi, D.; Cox, S.; Jong, W. A. de; Evans, M. L.; Gastellu, N.; Genzling, J.; Gil, M. V.; Gupta, A. K.; Hong, Z.; Imran, A.; Kruschwitz, S.; Labarre, A.; Lála, J.; Liu, T.; Ma, S.; Majumdar, S.; Merz, G. W.; Moitessier, N.; Moubarak, E.; Mouriño, B.; Pelkie, B.; Pieler, M.; Ramos, M. C.; Ranković B.; Rodriques, S. G.; Sanders, J. N.; Schwaller, P.; Schwarting, M.; Shi, J.; Smit, B.; Smith, B. E.; Herck, J. V.; Völker, C.; Ward, L.; Warren, S.; Weiser, B.; Zhang, S.; Zhang, X.; Zia, G. A.; Scourtas, A.; Schmidt, K. J.; Foster, I.; White, A. D.; Blaiszik, B. Digital Discovery 2023, 2, 1233.
[23]	Chithrananda, S.; Grand, G.; Ramsundar, B. ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction arXiv, 2020, https://doi.org/10.48550/arXiv.2010.09885.
[24]	Zhang, W.; Wang, Q.; Kong, X.; Xiong, J.; Ni, S.; Cao, D.; Niu, B.; Chen, M.; Li, Y.; Zhang, R.; Wang, Y.; Zhang, L.; Li, X.; Xiong, Z.; Shi, Q.; Huang, Z.; Fu, Z.; Zheng, M. 2024.
[25]	Schilling-Wilhelmi, M.; Ríos-García, M.; Shabih, S.; Gil, M. V.; Miret, S.; Koch, C. T.; Márquez, J. A.; Jablonka, K. M. Chem. Soc. Rev. 2025, 54, 1125.
[26]	Li, K.; Zhang, Y.; Wang, J.; Gan, H.; Sun, Z.; Lei, X.; Dong, H. ChemBART: A Pre-Trained BART Model Assisting Organic Chemistry Analysis arXiv, 2026, https://doi.org/10.48550/arXiv.2601.02915.
[27]	Dagdelen, J.; Dunn, A.; Lee, S.; Walker, N.; Rosen, A. S.; Ceder, G.; Persson, K. A.; Jain, A. Nat. Commun. 2024, 15, 1418.
[28]	Zhou, Z. H. Machine Learning, Tsinghua University Press, Beijing, 2019. (in Chinese)
	(周志华, 机器学习, 清华大学出版社, 北京, 2019.)
[29]	RDKit: Open-source cheminformatics.
[30]	Moriarty, A.; Kobayashi, T.; Salvalaglio, M.; Angeli, P.; Striolo, A.; McRobbie, I. J. Chem. Theory Comput. 2023, 19, 7371.
[31]	Shao, Y.; Xu, H.; You, F.; Li, Y.; Yang, Q.; Xue, X.-S. Chin. J. Chem. 2025, 43, 1363.
[32]	Huang, Z.; Yu, J.; He, W.; Yu, J.; Deng, S.; Yang, C.; Zhu, W.; Shao, X. J. Hazard. Mater. 2024, 465, 133355.
[33]	Dou, B.; Zhu, Z.; Merkurjev, E.; Ke, L.; Chen, L.; Jiang, J.; Zhu, Y.; Liu, J.; Zhang, B.; Wei, G.-W. Chem. Rev. 2023, 123, 8736.
[34]	Gao, B.; Cai, L.; Zhang, Y.; Huang, H.; Li, Y.; Xue, X.-S. CCS Chem. 2024.
[35]	Korolev, V.; Mitrofanov, A.; Korotcov, A.; T_Kachenko, V. J. Chem. Inf. Model. 2020, 60, 22.
[36]	Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural Message Passing for Quantum Chemistry arXiv, 2017, https://doi.org/10.48550/arXiv.1704.01212.
[37]	Stokes, J. M.; Yang, K.; Swanson, K.; Jin, W.; Cubillos-Ruiz, A.; Donghia, N. M.; MacNair, C. R.; French, S.; Carfrae, L. A.; Bloom- Ackermann, Z.; Tran, V. M.; Chiappino-Pepe, A.; Badran, A. H.; Andrews, I. W.; Chory, E. J.; Church, G. M.; Brown, E. D.; Jaakkola, T. S.; Barzilay, R.; Collins, J. J. Cell 2020, 180, 688.
[38]	Heid, E.; Greenman, K. P.; Chung, Y.; Li, S.-C.; Graff, D. E.; Vermeire, F. H.; Wu, H.; Green, W. H.; McGill, C. J. J. Chem. Inf. Model. 2024, 64, 9.
[39]	Guo, Q.; Hernandez-Hernandez, S.; Ballester, P. J. Scaffold Splits Overestimate Virtual Screening Performance,In Artificial Neural Networks and Machine Learning-ICANN 2024, Eds.: Wand, M.; Malinovská K.; Schmidhuber, J.; TeT_Ko, I. V., Springer Nature Switzerland, Cham, 2024, pp. 58-72. https://doi.org/10.1007/978-3-031-72359-9_5.
[40]	Lamens, A.; Bajorath, J. Chem. Sci. 2026, 17, 1411.
[41]	Huibers, P. D. T.; Lobanov, V. S.; Katritzky, A. R.; Shah, D. O.; Karelson, M. Langmuir 1996, 12, 1462.
[42]	Khayamian, T.; Esteki, M.; Abbasi, A. Application of Wavelet Neural Networks in Multivariate Data Analysis, In Progress in Chemometrics Research, Ed.: Pomerantsev, A. L., Nova Science Publishers, Inc., Hauppauge, 2005; pp. 37-41.
[43]	Baghban, A.; Bahadori, A. Pet. Sci. Technol. 2016, 34, 1691.
[44]	Baghban, A.; Sasanipour, J.; Sarafbidabad, M.; Piri, A.; Razavi, R. Chem. Phys. Lipids 2018, 214, 46.
[45]	Shi, Y.; Yan, F.; Jia, Q.; Wang, Q. Colloids Surf., A 2019, 583, 123967.
[46]	Jiao, L.; Wang, Y.; Qu, L.; Xue, Z.; Ge, Y.; Liu, H.; Lei, B.; Gao, Q.; Li, M. Colloids Surf., A 2020, 586, 124226.
[47]	Setiawan, E.; Wijaya, K.; Mudasir, M. AIP Conf. Proc. 2021, 2349, 20027.
[48]	Creton, B.; Barraud, E.; and Nieto-Draghi, C. SAR QSAR Environ. Res. 2024, 35, 309.
[49]	Qin, S.; Jin, T.; Van Lehn, R. C.; Zavala, V. M. J. Phys. Chem. B 2021, 125, 10610.
[50]	Seddon, D.; Cabral, J. T. J. Colloid Interface Sci. 2022, 625, 328.
[51]	Boukelkal, N.; Rahal, S.; Rebhi, R.; Hamadache, M. J. Mol. Graphics Modell. 2024, 129, 108757.
[52]	Brozos, C.; Rittig, J. G.; Bhattacharya, S.; Akanny, E.; Kohlmann, C.; Mitsos, A. Colloids Surf., A 2024, 694, 134133.
[53]	Brozos, C.; Rittig, J. G.; Bhattacharya, S.; Akanny, E.; Kohlmann, C.; Mitsos, A. J. Chem. Theory Comput. 2024, 20, 5695.
[54]	Chen, J.; Hou, L.; Nan, J.; Ni, B.; Dai, W.; Ge, X. Colloids Surf., A 2024, 703, 135276.
[55]	Hödl, S. L.; Hermans, L.; Dankloff, P. F. J.; Piruska, A.; Huck, W. T. S.; Robinson, W. E. Digital Discovery 2025, 4, 1176.
[56]	Ham, S.; Wang, X.; Zhang, H.; Lattimer, B.; Qiao, R. Colloids Interfaces 2024, 8, 63.
[57]	Barbosa, G. D.; Striolo, A. J. Chem. Eng. Data 2025, 70, 4019.
[58]	Brozos, C.; Rittig, J. G.; Akanny, E.; Bhattacharya, S.; Kohlmann, C.; Mitsos, A. Comput. Chem. Eng. 2025, 198, 109085.
[59]	Choudhary, A.; Desai, S.; Kamruzzaman, M.; Landera, A.; Ghosh, K.; Poorey, K. J. Cheminf. 2025, 17, 170.
[60]	Gaudin, T.; Lu, H.; Fayet, G.; Berthauld-Drelich, A.; Rotureau, P.; Pourceau, G.; Wadouachi, A.; Van Hecke, E.; Nesterenko, A.; Pezron, I. Adv. Colloid Interface Sci. 2019, 270, 87.
[61]	Marquez, R.; Ortiz, M. S.; Barrios, N.; Vera, R. E.; Patiño-Agudelo, Á. J.; Vivas, K. A.; Salas, M.; Zambrano, F.; Theiner, E. J. Surfactants Deterg. 2025, 28, 25.
[62]	Wang, Y.; Yan, F.; Jia, Q.; Wang, Q. J. Mol. Liq. 2018, 253, 205.
[63]	Luan, F.; Liu, H.; Gao, Y.; Li, Q.; Zhang, X.; Guo, Y. J. Colloid Interface Sci. 2009, 336, 773.
[64]	Katritzky, A. R.; Pacureanu, L.; Dobchev, D.; Karelson, M. J. Chem. Inf. Model. 2007, 47, 782.
[65]	Setiawan, E.; Wijaya, K. CHEMRXIV 2020.
[66]	Laidi, M.; Abdallah, E.; Si-Moussa, C.; Benkortebi, O.; Hentabli, M.; Hanini, S. Chem. Ind. Chem. Eng. Q. 2021, 27, 299.
[67]	Ricardo, F.; Ruiz-Puentes, P.; Reyes, L. H.; Cruz, J. C.; Alvarez, O.; Pradilla, D. Chem. Eng. Sci. 2023, 265, 118208.
[68]	Mulka, R.; Su, D.; Huang, W.-S.; Zhang, L.; Huang, H.; Lai, X.; Li, Y.; Xue, X. J. Cheminf. 2025, 17, 19.
[69]	Liu, X.; Zhao, Y.; Li, Q.; Jiao, T.; Niu, J. J. Mol. Liq. 2016, 219, 1100.
[70]	Wang, Z.; Li, Gan-Zuo; Mu, J.-H.; Zhang, X. Chin. Chem. Lett. 2002, 13, 363.
[71]	Wang, Z.; Huang, D.; Li, G.; Zhang, X.; Liao, L. J. Dispersion Sci. Technol. 2003, 24, 653.
[72]	Wang, Z.; Feng, J.; Wang, H.; Cui, Z.; Li, G. J. Dispersion Sci. Technol. 2005, 26, 441.
[73]	Gaudin, T.; Rotureau, P.; Pezron, I.; Fayet, G. J. Colloid Interface Sci. 2018, 516, 162.
[74]	Soleimani, R.; Saeedi Dehaghani, A. H. Sci. Rep. 2023, 13, 14145.
[75]	Dong, Q.; Muzny, C. D.; Kazakov, A.; Diky, V.; Magee, J. W.; Widegren, J. A.; Chirico, R. D.; Marsh, K. N.; Frenkel, M. J. Chem. Eng. Data 2007, 52, 1151.
[76]	Rashidi-Khaniabadi, A.; Rashidi-Khaniabadi, E.; Amiri-Ramsheh, B.; Mohammadi, M.-R.; Hemmati-Sarapardeh, A. Sci. Rep. 2023, 13, 10836.
[77]	Qing, D.; Lu, Z.; Hou, L.; Dai, W.; Hong, J.; Ge, X. Colloids Surf., A 2025, 727, 138427.
[78]	Tanford, C. Proc. Natl. Acad. Sci. U. S. A. 1979, 76, 4175.
[79]	Rosen, M. J. J. Am. Oil Chem. Soc. 1974, 51, 461.
[80]	Gaudin, T.; Rotureau, P.; Pezron, I.; Fayet, G. Int. J. Quant. Struct.- Prop. Relatsh. 2019, 4, 28.
[81]	Abbott, S. Surfactant Science: Principles and Practice, Destech Pubns Inc, 2017.
[82]	Li, S.; Mao, X.; Cao, X.; Feng, Y.; Zhang, Y.; Yin, H. Colloids Surf., A 2025, 712, 136400.
[83]	Bregni, C. Lat. Am. J. Pharm 2009.
[84]	Beteringhe, A.; Răduţiu, A. C.; Bem, M.; Bărăţoiu, R. D.; Hristea, E.-N.; Constantinescu, T. Rev. Roum. Chim. 2011, 56, 347.
[85]	Gad, E. A. M.; Khairou, K. S. J. Dispersion Sci. Technol. 2008, 29, 940.
[86]	Chen, M.-L.; Wang, Z.-W.; Duan, H.-J. J. Dispersion Sci. Technol. 2009, 30, 1481.
[87]	Wu, J.; Yan, F.; Jia, Q.; Wang, Q. Colloids Surf., A 2021, 611, 125812.
[88]	Tsujii, K. Ph.D. Dissertation, Osaka University, Osaka, Japan, 1983.
[89]	Huibers, P. D. T.; Shah, D. O.; Katritzky, A. R. J. Colloid Interface Sci. 1997, 193, 132.
[90]	Mehdi, J.-H.; Konoz, E. Internet Electron. J. Mol. Des. 2002, 1.
[91]	Li, Y.; Xu, G.; Luan, Y.; Yuan, S.; Xin, X. J. Dispersion Sci. Technol. 2005, 26, 799.
[92]	Gaudin, T.; Fayet, G.; Rotureau, P.; Pezron, I. J. Surfactants Deterg. 2018, 21, 835.
[93]	Qi, X.; Hu, Y.; Wang, R.; Yang, Y.; Zhao, Y. Acta Chim. Sin. 2023, 81, 158. (in Chinese)
	(戚兴怡, 胡耀峰, 王若愚, 杨雅清, 赵宇飞, 化学学报, 2023, 81, 158.)
[94]	Pillai, N.; Dasgupta, A.; Sudsakorn, S.; Fretland, J.; Mavroudis, P. D. Drug Discovery Today 2022, 27, 2209.
[95]	Martinelli, D. D. Comput. Biol. Med. 2022, 145, 105403.
[96]	Liu, Y. D.; Yang, Q.; Li, Y; Zhang, L.; Luo, S. Z. Chin. J. Org. Chem. 2020, 40, 3812. (in Chinese)
	(刘伊迪, 杨骐, 李遥; 张龙, 罗三中, 有机化学, 2020, 40, 3812.)
[97]	Zhu, B. Y.; Wu, R. L.; Yu, X. Acta Chim. Sinica 2020, 78, 1366. (in Chinese)
	(朱博阳, 吴睿龙, 于曦, 化学学报, 2020, 78, 1366.)
[98]	Nnadili, M.; Okafor, A. N.; Olayiwola, T.; Akinpelu, D.; Kumar, R.; Romagnoli, J. A. Ind. Eng. Chem. Res. 2024, 63, 6313.
[99]	Xue, D.; Gong, Y.; Yang, Z.; Chuai, G.; Qu, S.; Shen, A.; Yu, J.; Liu, Q. Wires Comput. Mol. Sci. 2019, 9, e1395.
[100]	Krenn, M.; Häse, F.; Nigam, A.; Friederich, P.; Aspuru-Guzik, A. Mach. Learn.: Sci. Technol. 2020, 1, 45024.
[101]	González-Núñez, S.; Sumer, Z.; Amador, C.; Madhav, P.; Muralidharan, A.; Adjiman, C. S.; Martín, M. ACS Sustainable Chem. Eng. 2025, 13, 13808.
[102]	Zhang, Y.; Huang, M.; Liu, X.; Zheng, J.; Xu, Y.; Guo, Y. Phys. Fluids 2025, 37, 012123.
[103]	Devatha, T.; Gajjar, J. Int. J. Sci. Res. (IJSR) 2023, 12, 492.
[104]	Hussain, S. M. S.; Adewunmi, A. A.; Mahboob, A.; Murtaza, M.; Zhou, X.; Kamal, M. S. Adv. Colloid Interface Sci. 2022, 303, 102634.
[105]	Lai, T. T.; Kuntz, D.; Wilson, A. K. J. Chem. Inf. Model. 2022, 62, 4569.